High-speed calculation method of the Hurst parameter based on real traffic
Tatsuya Hagiwara, Hiroki Doi, Hideki Tode, Hiromasa Ikeda
Abstract
Tatsuya Hagiwara, Hiroki Doi, Hideki Tode, Hiromasa Ikeda
Abstract
Previous studies on traffic measurement analysis in the various networks have shown that packet traffic exhibits long range dependent properties called self-similarity. Some papers reported that self-similarity degrades the network performance, such as buffer overflow. Thus, we need new network control considering self-similar properties. Network control considering the self-similarity requires high speed calculation method of the Hurst parameter. However, such a method has not been proposed yet. In this paper, we propose high-speed calculation method of the Hurst parameter based on the variance-time plot method, and show its performance. Furthermore, we try to show the effectiveness of the network control with self-similarity.
OpenAlex reports 6 citations for this work. Citation counts describe recorded attention and do not establish research quality.
A contribution statement is not available in the OpenAlex record.
Method details are not available in the OpenAlex metadata.
Findings are not separately available in the OpenAlex metadata.
Limitations are not available in the OpenAlex metadata.
Application details are not available in the OpenAlex metadata.
Previous studies on traffic measurement analysis in the various networks have shown that packet traffic exhibits long range dependent properties called self-similarity. Some papers reported that self-similarity degrades the network performance, such as buffer overflow. Thus, we need new network control considering self-similar properties. Network control considering the self-similarity requires high speed calculation method of the Hurst parameter. However, such a method has not been proposed yet. In this paper, we propose high-speed calculation method of the Hurst parameter based on the variance-time plot method, and show its performance. Furthermore, we try to show the effectiveness of the network control with self-similarity.
Key concepts: Hurst exponent, Self-similarity, Computer science, Similarity (geometry), Network packet, Range (aeronautics), Variance (accounting), Data mining